Farmers' perspective on digitalization of climate-smart agricultural practices: a comparative study in Tamil Nadu, India
Bibliographic record
Abstract
Adopting digitalization of climate-smart agricultural (DCSA) practices is expected to promote improved adaptation, mitigation, and productivity in agricultural activities of small landholding farmers. Understanding the perceptions and influential factors that influence farmers' adoption of DCSA services is crucial to promoting DCSA services and making farmers adept at tackling climate change's impact in the future from the policymakers’ perspective. Through semi-structured interviews and focus group discussions, we studied the perception of DCSA amongst two sets of farmers (Type A: government-led extension functionaries; Type B: supported by a local change agent). To reveal the similarities and differences, we categorize the two sets of farmers’ responses under attributes of diffusion of innovation theory (relative advantage, compatibility, complexity, trialability, and communicability). We found that the better adoption of DCSA practices is attributed to the intermediary role played by institutions and local change agents in providing relevant support and enabling farmers to adopt DCSA practices seamlessly. Our findings contribute to speeding up the dissemination of agricultural information and increasing farmers’ adoption of DCSA services. To the best of our knowledge, this study is the first to examine influential factors for DCSA adoption from the standpoint of Diffusion of Innovation's theory attributes of technology in the Tamil Nadu case study area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".